Measuring Masking Fault-Tolerance

Castro, Pablo F.; D’Argenio, Pedro R.; Demasi, Ramiro; Putruele, Luciano

doi:10.1007/978-3-030-17465-1_21

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…Hence, the proposed distance measures how many faults are tolerated by I while being masked by the states of the system. Notice that the notion of robustness from [CHR12] and the masking fault-tolerant distance from [CDDP19] are quite different from our notions of reliability and robustness. In fact, we are not interested in counting how many times an error occurs, but in checking whether the system is able to regain the desired behaviour after the occurrence of an error.…”

Section: Discussionmentioning

confidence: 83%

“…In [CHR12] the authors do so by setting a two players game with weighted choices, and the cost of the game is interpreted as the distance between I and S. Hence the authors propose three distance functions: correctness, coverage, and robustness. Correctness expresses how often I violates S, coverage is its dual,and robustness measures how often I can make an unexpected error with the resulting behaviour still meeting S. A similar game-based approach is used in [CDDP19] to define a masking fault-tolerant distance. Briefly, a system is masking fault-tolerant if faults do not induce any observable behaviour in the system.…”

Section: Discussionmentioning

confidence: 99%

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A framework to measure the robustness of programs in the unpredictable environment

Castiglioni¹,

Loreti²,

Tini³

2023

Logical Methods in Computer Science

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Due to the diffusion of IoT, modern software systems are often thought to control and coordinate smart devices in order to manage assets and resources, and to guarantee efficient behaviours. For this class of systems, which interact extensively with humans and with their environment, it is thus crucial to guarantee their correct behaviour in order to avoid unexpected and possibly dangerous situations. In this paper we will present a framework that allows us to measure the robustness of systems. This is the ability of a program to tolerate changes in the environmental conditions and preserving the original behaviour. In the proposed framework, the interaction of a program with its environment is represented as a sequence of random variables describing how both evolve in time. For this reason, the considered measures will be defined among probability distributions of observed data. The proposed framework will be then used to define the notions of adaptability and reliability. The former indicates the ability of a program to absorb perturbation on environmental conditions after a given amount of time. The latter expresses the ability of a program to maintain its intended behaviour (up-to some reasonable tolerance) despite the presence of perturbations in the environment. Moreover, an algorithm, based on statistical inference, is proposed to evaluate the proposed metric and the aforementioned properties. We use two case studies to the describe and evaluate the proposed approach.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

A framework to measure the robustness of programs in the unpredictable environment

Castiglioni¹,

Loreti²,

Tini³

2023

Logical Methods in Computer Science

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“…We start this section by defining a probabilistic extension of the strong masking simulation introduced in [8]. Roughly speaking, this is a variation of probabilistic bisimulation that takes into account the occurrence of faults (named masking simulation), and captures masking behavior.…”

Section: Probabilistic Masking Simulationmentioning

confidence: 99%

“…Some authors have already pointed out that these metrics can be useful to reason about the robustness and correctness of a system, notions related to fault-tolerance. Here we follow the ideas introduced in [8] where masking fault-tolerance is captured by means of a tailored bisimulation game with quantitative objectives. We extend these ideas to a probabilistic setting and define a probabilistic version of this characterization of masking fault-tolerance which, in turn, we use to define a metric to compare the "degree" of masking fault tolerance provided by different mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Masking Fault-Tolerance via Fair Stochastic Games

Castro,

D'Argenio,

Demasi

et al. 2023

Electron. Proc. Theor. Comput. Sci.

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We introduce a formal notion of masking fault-tolerance between probabilistic transition systems using stochastic games. These games are inspired in bisimulation games, but they also take into account the possible faulty behavior of systems. When no faults are present, these games boil down to probabilistic bisimulation games. Since these games could be infinite, we propose a symbolic way of representing them so that they can be solved in polynomial time. In particular, we use this notion of masking to quantify the level of masking fault-tolerance exhibited by almost-sure failing systems, i.e., those systems that eventually fail with probability 1. The level of masking fault-tolerance of almost-sure failing systems can be calculated by solving a collection of functional equations. We produce this metric in a setting in which one of the player behaves in a strong fair way (mimicking the idea of fair environments).

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